Purpose :
Selection of individuals with traits of interest that fall to opposite extremes of a spectrum are important in order to maximize the power of small studies. In cases of rare diseases where multiple phenotypes contribute to the disease definition and where there may be few individuals at these extremes, it is useful to take into account multiple outlier categories. However, incorporation of additional categories can introduce heterogeneity into the study population. We consider the case of retinopathy of prematurity (ROP) and selection of traits from a set including birth weight, gestational age, disease burden, and twin status.

Methods :
A multicenter database was generated prospectively from infants screened for ROP at one of 6 major ROP centers in the United States. The database was reviewed to identify infants treated for ROP. Database information included level of ROP progression, treatment required, physical factors (birth weight, postmenstrual age), demographic data, and presence of additional disease factors (sepsis, necrotizing enterocollitis, chronic lung disease, death during hospital stay). Percentile weight for gestational age was calculated using gestational age and birth weight. Expert clinicians were consulted to determine weights reflecting the importance of each trait in the determination of interest as a genetic study participant.

Results :
406 Caucasian infants representing 1603 exams were identified from the full database. A weighting matrix was applied to the data and a ranked list of infants according to genetic interest was generated from the weighted data. Permutations of the ranked data were visualized and reviewed by a panel of expert clinicians and genetic specialists to determine a combination of traits leading to ideal separation in sample and collinearity of existing traits. The ideal factors indicating outlier status were determined to be birth weight, level of treatment, and degree of ROP.

Conclusions :
Complex models provide context about the sample population, but incorporation of competing additional traits limits the ability to sample population extremes. Therefore in studies of limited sample size where maximum power is required to detect an effect, it is often necessary to limit the number of factors contributing to phenotypic interest. In retinopathy of prematurity, the highest priority information for outlier status is captured using the three factors listed above.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.